{"id":"W2963379170","doi":"10.1109/crv.2018.00026","title":"WAYLA - Generating Images from Eye Movements","year":2018,"lang":"en","type":"article","venue":"","topic":"Image Processing Techniques and Applications","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Artificial intelligence; Computer vision; Computer science; Image translation; Translation (biology); Image (mathematics); Eye tracking; Gaze; Image segmentation; Segmentation; Process (computing)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00002504179,0.00005967461,0.00004432847,0.00001557576,0.00007092021,0.00005192789,0.00008897826,0.0000235254,0.0003331802],"category_scores_gemma":[0.000003179692,0.0000551274,0.00001252622,0.00005571613,0.00002053333,0.00007631504,0.00002414603,0.00003880264,0.0001212268],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001168382,"about_ca_system_score_gemma":0.000002616359,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005045097,"about_ca_topic_score_gemma":0.000004008152,"domain_scores_codex":[0.999685,0.000001815663,0.00008262239,0.00008504376,0.00004537146,0.0001000731],"domain_scores_gemma":[0.9998069,0.000004501333,0.000008314879,0.0001330545,0.00002569911,0.00002153732],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[5.026312e-7,0.00001516477,0.0003375128,0.000009494577,0.00001610011,5.796297e-7,0.0001150277,0.0001053957,0.9043365,0.0004506557,0.02840341,0.06620964],"study_design_scores_gemma":[0.00005865749,0.00000686084,0.0002840321,0.000009482044,0.000003276978,1.486563e-7,0.0000163375,0.138234,0.8416444,0.002174567,0.0174479,0.0001203782],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0894227,0.0001006156,0.8460213,0.00007092286,0.00007049152,0.00006842001,0.00001191763,0.001285643,0.06294797],"genre_scores_gemma":[0.7000437,0.000009374446,0.2982446,0.0001872327,0.0002430703,0.00002568158,0.00001116871,0.00001742441,0.001217703],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.610621,"threshold_uncertainty_score":0.3648089,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01040029767378178,"score_gpt":0.2564120494258525,"score_spread":0.2460117517520707,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}